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TU Berlin

Inhalt des Dokuments

Publications

You can find below a list of my publications.

You can also visit my google scholar profile [link]

Journal publications

Lindhorst, H., Lucia, S., Findeisen, R. and Waldherr, S. (2019). Modeling Enzyme Controlled Metabolic Networks in Rapidly Changing Environments by Robust Optimization. IEEE Control Systems Letters, 248–253.


Lucia, S., Navarro, D., Lucia, O., Zometa, P. and Findeisen, R. (2018). Optimized FPGA Implementation of Model Predictive Control Using High Level Synthesis Tools. IEEE Transactions on Industrial Informatics, 137–145.


Thangavel, S., Lucia, S., Paulen, R. and Engell, S. (2018). Dual Robust Nonlinear Model Predictive Control: A Multi-stage Approach. Journal of Process Control, 39–51.


Lucia, S., Navarro, D., Karg, B., Sarnago, H. and Lucia, O. (2018). Deep Learning-based Model Predictive Controlfor Resonant Power Converters. IEEE Transactions on Industrial Informatics (submitted), 137–145.


Karg, B. and Lucia, S. (2018). Efficient representation and approximation of model predictive control laws via deep learning. arXiv preprint arXiv:1703.02702


Lucia, S., Tatulea-Codrean, A., Schoppmeyer, C. and Engell, S. (2017). Rapid Development of Modular and Sustainable Nonlinear Model Predictive Control Solutions. Control Engineering Practice, 51-62.


Lucia, S., Kögel, M., Zometa, P., Quevedo, D. E. and Findeisen, R. (2016). Predictive control, embedded cyberphysical systems and systems of systems – A perspective. Annual Reviews in Control, 193–207.


Marti, R., Lucia, S., Sarabia, D., Paulen, R., Engell, S. and de Prada, C. (2015). Improving scenario decomposition algorithms for robust nonlinear model predictive control. Computers & Chemical Engineering, 30 - 45.


Lucia, S., Andersson, J., Brandt, H., Diehl, M. and Engell, S. (2014). Handling Uncertainty in Economic Nonlinear Model Predictive Control: a Comparative Case-study. Journal of Process Control, 1247-1259.


Finkler, T., Lucia, S., Dogru, M. and Engell, S. (2013). Simple Control Scheme for Batch Time Minimization of Exothermic Semibatch Polymerizations. Industrial & Engineering Chemistry Research, 5906-5920.


Lucia, S., Finkler, T. and Engell, S. (2013). Multi-stage Nonlinear Model Predictive Control Applied to a Semi-batch Polymerization Reactor under Uncertainty. Journal of Process Control, 1306-1319.


Conference publications

Economic Multi-stage Output Feedback NMPC using the Unscented Kalman Filter
Citation key subramanian2015_adchem
Author S. Subramanian and Lucia, S. and S. Engell
Title of Book Proc. of the 9th IFAC Symposium on Advanced Control of Chemical Processes
Pages 38 - 43
Year 2015
Abstract Nonlinear Model predictive control (NMPC) is a popular control strategy for highly nonlinear chemical processes. The ability to handle safety and environmental constraints along with the use of an economic objective makes NMPC highly appealing to industries. The performance of NMPC depends strongly on the accuracy of the model. In reality, there always are plant-model mismatch and state estimation errors. Hence the NMPC controller must be robust to uncertainties in the model as well as against estimation errors. Among the several approaches presented in the literature, the scenario-tree based multi-stage NMPC approach is a non-conservative and efficient formulation. In this approach, the evolutions of the plant for different realizations of the uncertainties are considered as different scenarios and the optimization problem is formulated as a multi-stage stochastic programming problem with recourse. In this work, we consider multi-stage output feedback NMPC using the Unscented Kalman Filter (UKF) where the nonlinearities are represented using deterministically chosen sigma points for state estimation. In the control problem, we explicitly consider the UKF estimation equations to predict the future evolution of the system. The proposed approach is illustrated by simulation results of fed-batch chemical reactor with an economic cost function.
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Dissertation

You can download a copy of my Dissertation, entitled Robust Multi-stage Nonlinear Model Predictive Control, clicking here

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